# Expanding the GoT toolkit to link single-cell clonal genotypes with protein, transcriptomic, epigenomic and spatial phenotypes

> **NIH NIH R33** · WEILL MEDICAL COLL OF CORNELL UNIV · 2024 · $398,216

## Abstract

Abstract
Clonal outgrowths are observed across a wide range of normal human tissues. They also appear during the
course of cancer evolution, leading to clonal heterogeneity that fuels the development of treatment-resistant
disease. Clones harbor somatic mutations in known cancer driver genes and show evidence of positive
selection. Nevertheless, how these driver mutations alter the cellular states of cells to allow clones to
outcompete wildtype counterparts remains poorly understood. To date, efforts to chart clonal outgrowths in
normal or malignant human tissues have been largely limited to genotyping. This is due to the fact that these
clones often affect a minority of cells in a sample without distinguishing cell-surface markers.
To address this challenge, we developed an array of multi-omic single-cell technologies that are capable of
capturing multiple layers of information (e.g., genotypes, transcriptomes, methylomes, protein expression) from
the same single cells. Moreover, we addressed the specific challenge of genotyping in scRNA-seq in single
cells at high throughput by developing Genotyping of Targeted loci (GoT). Importantly, GoT turns the admixture
of mutant and wildtype hematopoiesis from a limitation to an advantage, enabling the direct comparison of
mutant (“winner”) and wildtype (“loser”) cells within the same individual.
Given the increasing adoption of our GoT platform, we now aim to extend the multi-omics single-cell toolkit to
study how somatic mutations lead to clonal growth advantage. We will integrate GoT with Cellular Indexing of
Transcriptomes and Epitopes by sequencing (CITE-seq) to yield GoT-CITE, which will add the critical layer of
cell surface marker phenotyping to single-cell whole transcriptomes. As mutations in splicing factors are
specifically associated with greater risk of malignant transformation, we will develop and implement GoT-
Splice, where long-read sequencing will be used to define splicing variation as a function of cell identity. Given
the high frequency of epigenetic mutations in cancer, we will also develop and apply targeted single-cell
genotyping in the context of chromatin accessibility (GoT-ChA). Finally, as clone growth will also be
determined by its interaction with the microenvironment, to define clonal driver genotypes in its spatial context,
we will adapt spatial transcriptomics (ST) to add the critical feature of genotyping (GoT-ST).
Our overarching goal is to invoke multi-omic comparisons at the single-cell level between wildtype and mutant
cells to comprehensively identify the underpinnings of fitness advantage in clonal outgrowth. The proposed
comprehensive GoT toolkit will enable the linking, at high throughout, single-cell genotypes with transcriptional,
protein, epigenetic and spatial phenotypes. We anticipate that these advances will transform the study of clonal
mosaicism as a harbinger of cancer, as well as resistance to cancer therapies.

## Key facts

- **NIH application ID:** 10886692
- **Project number:** 5R33CA267219-03
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Dan Landau
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $398,216
- **Award type:** 5
- **Project period:** 2022-09-07 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10886692

## Citation

> US National Institutes of Health, RePORTER application 10886692, Expanding the GoT toolkit to link single-cell clonal genotypes with protein, transcriptomic, epigenomic and spatial phenotypes (5R33CA267219-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10886692. Licensed CC0.

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